lab_6

ca_dgw <- read_sf(here("ca_dgw"), layer = "F2013_DBGS_Points_20150720_093252") %>% 
  clean_names()

st_crs(ca_dgw)

## Coordinate Reference System:
##   User input: WGS 84 
##   wkt:
## GEOGCRS["WGS 84",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4326]]

ca_counties <- read_sf(here("ca_counties"), layer = "CA_Counties_TIGER2016") %>% 
  clean_names() %>% 
  select(name)

st_crs(ca_counties)

## Coordinate Reference System:
##   User input: WGS 84 / Pseudo-Mercator 
##   wkt:
## PROJCRS["WGS 84 / Pseudo-Mercator",
##     BASEGEOGCRS["WGS 84",
##         DATUM["World Geodetic System 1984",
##             ELLIPSOID["WGS 84",6378137,298.257223563,
##                 LENGTHUNIT["metre",1]]],
##         PRIMEM["Greenwich",0,
##             ANGLEUNIT["degree",0.0174532925199433]],
##         ID["EPSG",4326]],
##     CONVERSION["Popular Visualisation Pseudo-Mercator",
##         METHOD["Popular Visualisation Pseudo Mercator",
##             ID["EPSG",1024]],
##         PARAMETER["Latitude of natural origin",0,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8801]],
##         PARAMETER["Longitude of natural origin",0,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8802]],
##         PARAMETER["False easting",0,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8806]],
##         PARAMETER["False northing",0,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8807]]],
##     CS[Cartesian,2],
##         AXIS["easting (X)",east,
##             ORDER[1],
##             LENGTHUNIT["metre",1]],
##         AXIS["northing (Y)",north,
##             ORDER[2],
##             LENGTHUNIT["metre",1]],
##     USAGE[
##         SCOPE["unknown"],
##         AREA["World - 85°S to 85°N"],
##         BBOX[-85.06,-180,85.06,180]],
##     ID["EPSG",3857]]

ca_counties <- st_transform(ca_counties, st_crs(ca_dgw))

st_crs(ca_counties)

## Coordinate Reference System:
##   User input: WGS 84 
##   wkt:
## GEOGCRS["WGS 84",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4326]]

ggplot() +
  geom_sf(data = ca_counties) +
  geom_sf(data = ca_dgw, aes(color = dgbs))

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(ca_dgw) +
  tm_dots("dgbs")

## Variable(s) "dgbs" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

sj_county <- ca_counties %>% 
  filter(name == "San Joaquin")

sj_depth <- ca_dgw %>% 
  st_intersection(sj_county)

## although coordinates are longitude/latitude, st_intersection assumes that they are planar

## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries

plot(sj_depth)

## Warning: plotting the first 10 out of 18 attributes; use max.plot = 18 to plot
## all

ggplot() +
  geom_sf(data = sj_county) +
  geom_sf(data = sj_depth, aes(color = dgbs))

well_duplicate <- sj_depth %>% 
  get_dupes(latitude, longitude)

sj_depth <- sj_depth %>% 
  filter(!local_well %in% well_duplicate$local_well)

sj_depth %>% 
  get_dupes(latitude, longitude)

## No duplicate combinations found of: latitude, longitude

## Simple feature collection with 0 features and 19 fields
## bbox:           xmin: NA ymin: NA xmax: NA ymax: NA
## geographic CRS: WGS 84
## # A tibble: 0 x 20
## # … with 20 variables: latitude <dbl>, longitude <dbl>, dupe_count <int>,
## #   site_code <chr>, local_well <chr>, state_well <chr>, wcr_number <chr>,
## #   well_use <dbl>, msmt_date <date>, msmt_agenc <dbl>, wsel <dbl>, dgbs <dbl>,
## #   rp_elevati <dbl>, gs_elevati <dbl>, msmt_metho <dbl>, msmt_issue <dbl>,
## #   msmt_comme <chr>, link_to_wd <chr>, name <chr>, geometry <GEOMETRY [°]>

Creat a variagram

sj_dgw_vgm <- variogram(dgbs ~ 1, data = sj_depth)

plot(sj_dgw_vgm)

sj_dgw_vgm_fir <- fit.variogram(sj_dgw_vgm, model = vgm(nugget = 20, psill = 3000, range = 30, model = "Gau") )

sj_dgw_vgm_fir

##   model     psill    range
## 1   Nug  102.3052  0.00000
## 2   Gau 2843.6996 17.18188

plot(sj_dgw_vgm, sj_dgw_vgm_fir)

Spacial Kriging

sj_grid <- st_bbox(sj_county) %>% 
  st_as_stars(dx = 0.01, dy = 0.01) %>% 
  st_set_crs(4326) %>% 
  st_crop(sj_county)

## although coordinates are longitude/latitude, st_union assumes that they are planar

## although coordinates are longitude/latitude, st_intersects assumes that they are planar

sj_dgw_krig <- krige(dgbs ~ 1, sj_depth, sj_grid, model = sj_dgw_vgm_fir)

## [using ordinary kriging]

plot(sj_dgw_krig)